Summary

Bokeh is a Python visualization library for large datasets that natively
uses the latest web technologies. Its goal is to provide concise
construction of novel graphics, while delivering high-performance
interactivity over large data to thin clients. This talk will cover the
motivation and architecture behind Bokeh, demonstrate interesting uses
and capability, and discuss future plans.

Description

With support from the DARPA XDATA Initiative, and contributions from
community members, the Bokeh visualization library
(http://bokeh.pydata.org) has grown into a large, successful open source
project with heavy interest and following on GitHub
(https://github.com/ContinuumIO/bokeh). The principal goals of Bokeh are
to provide capability to developers and domain experts:

easily create novel and powerful visualizations

that extract insight from remote, possibly large data sets

published to the web for others to explore and interact

This talk will describe how the architecture of Bokeh enables these
goals, and demonstrate how it can be leveraged by anyone using python
for analysis to visualize and present their work. We will talk about
current development and future plans, including a brief discussion of
Joseph Cottam's exciting academic work on abstract rendering for large
data sets that is going into Bokeh
(https://github.com/JosephCottam/AbstractRendering).